Lungs are vital organs that are susceptible to airborne infection. People usually ignore the health of their respiratory system. Asthma is the most prevalent lung disorder in the world (about 240 million), after that chronic obstructive pulmonary disease (about 200 million). Besides, about 235 thousand cases have been reported for lung cancer in the US in one year. The mortality rate doubled in 14 years due to smoking, pollution, fumes, allergens and microbes.
Current respiratory function tests fail to detect some lung disorders in their early phases. Deep learning is a branch of machine learning science. It supports the supervised or unsupervised learning algorithms. It is based on extracting the features from different forms of data and using them in the classification process. Besides, it produces outcomes more quickly than the ordinary approaches.
Our study aimed to develop a deep Learning model to differentiate between lung dysfunction patients and healthy persons. If there was a disease, it would classify the type of malfunction even in its first stage.
This study included 8128 images collecting the air flow information in a transversal section inside the trachea. These images were divided into two types of datasets. The first type was only a picture of the collected air particles in a specific section in the trachea. The second type was heat map images for the flow of the air through 24 hours. Each set was divided into 4 groups including non-patient group beside other 3 different types of disorders. In order to use the deep learning in diagnosis, two different of Convolution neural network CNN models were used. CNN is a branch in deep learning which is specific in processing images. Each group was divided into training and testing sets. The CNN models where trained to learn the features in the images having the best training accuracy. The trained model was evaluated by calculating the performance accuracy of the testing datasets.
The results showed that CNN models could diagnose the lung disorders by accuracy 95.77% and 93.5% for the heatmap and particles’ models. Thus, deep learning managed to diagnose lung disorders using airflow images inside the trachea.